In many signal processing scenarios, multiple signal sources are simultaneously present, and any sensor placed in the vicinity of these sources will respond to a mixture of the active sources, rather than any single individual source. In such scenarios, the signal processing environment is the space, whether physical or virtual, in which the sources and sensors are placed, and through which signals propagate. Thus for instance, in the case of acoustic sources, the environment might be a room, and the sensors might be microphones placed in the room. The sources might be speakers (either humans or loudspeakers), telephones, etc.
For example, referring to FIG. 3A, an example is shown of a system 300 that includes a plurality of sources 302a-c and a plurality of sensors 306a-c. Although the system 300 is shown as including four sources 302a-c and three sensors 306a-c, the particular numbers of sources and sensors shown in FIG. 3A is merely an example and does not constitute a limitation of the present invention, which may be used in connection with any number of sources and any number of sensors, and any number of mixture components. The number of sources need not, in general, be equal to the number of sensors.
The sources 302a-c emit corresponding signals 304a-c. More specifically, source 302a emits signal 304a, source 302b emits signal 304b, and source 302c emits signal 304c. Although in FIG. 3A each of the sources 302a-c is shown as emitting exactly one signal, this is merely an example and does not constitute a limitation of the present invention, which may be used in connection with sources that that emit any number of signals.
In FIG. 3A, each of the sensors 306a-c receives a mixture of two or more of the signals 304a-c. In practice, any particular sensor may receive zero, one, two, or more signals. In the particular example of FIG. 3A, sensor 306a receives a mixture of signals 304a and 304b; sensor 306b receives a mixture of signals 304b and 304c; and sensor 306c receives solely signal 304b. 
A signal source that contributes a mixture component with a statistically significant amount of energy to at least one sensor is called a contributing source. A source may be non-contributing either because it is inactive (not emitting a signal with any significant amount of energy) or because its location in the environment, the signal propagation properties of the environment, and/or the location of the sensors in the environment combine to shield all sensors from its contribution. Additional factors that typically determine whether a particular source is contributing or not include the spectral content of the source signal, the transfer function of the environment, and the frequency response of the sensors.
The sensors 306a-c produce corresponding outputs 308a representing their input mixtures. These outputs are also called “responses” or “response signals.” For example, sensor 306a produces output 308a representing the mixture of signals 304a and 304b received by sensor 306a; sensor 306b produces output 308b representing the mixture of signals 304b and 304c received by sensor 306b; and sensor 306c produces output 308c representing the signal 304b received by sensor 306c. 
Although not specifically illustrated in FIG. 3A, the contribution that a particular signal makes to the mixture received by the sensors 306a-c may vary from source to source. For example, although in FIG. 3A both sensors 306a and 306b are shown as receiving signal 304b, properties of the signal 304b may in practice differ at sensor 306a and 306b, such as due to distances in distance traveled or other factors that dampen or otherwise modify the signal 304b on its way to sensors 306a and 306b. In many systems, there is a linear relationship between a source signal and the corresponding mixture component in the response of a particular sensor. This linear relationship can be described using a so-called “transfer function” that describes the propagation characteristics between the source and the sensor.
In many cases it would be advantageous to determine, or estimate, what each of the individual source signals 304a-c is. Techniques of processing sensor signals (which are mixtures) to separate sources from each other, are referred to as “Blind Source Separation” (BSS) algorithms. Here, the word “Blind” means that the only information available to the source separation system about the sources are the sensor responses—all of which are, in general, linear weighted mixtures of multiple sources. In other words, no “hidden” information about the sources themselves is available to the source separation system. The field of BSS processing is an active field of research—see, for instance, Aichner, et al (R. Aichner, H. Buchner, F. Yan, and W. Kellerman, “A real-time blind source separation scheme and its application to reverberant and noisy acoustic environments”, Signal Processing, vol. 86, pp. 1260-1277, 2007) for a detailed description of a BSS algorithm.
As applied to FIG. 3A, for example, BSS may be used in an attempt to process the outputs 308a-c of sensors 306a-c, respectively, to identify the source signals 304a-c. For example, the system 300 of FIG. 3A includes a blind source separation module 310 which receives the signals 308a-c output by the sensors 306a-c and generates, based solely on those sensor outputs 308a-c, source identification outputs 312a-c which are intended to identify the source signals 304a-c that caused the sensors 306a-c to produce the outputs 308a-c. For example, the blind source separation module 310 may be used to process outputs 308a, 308b, and 308c (e.g., simultaneously) to produce outputs 312a-c, where output 312a is intended to estimate source signal 304a; output 312b is intended to estimate source signal 304b; and output 312c is intended to estimate source signal 304c. 